Triple

T29546880
Position Surface form Disambiguated ID Type / Status
Subject Universiti Malaysia Kelantan Jeli Campus E749653 entity
Predicate campusOf P269 FINISHED
Object Universiti Malaysia Kelantan
Universiti Malaysia Kelantan is a Malaysian public university known for its focus on entrepreneurship education and regional development in the state of Kelantan.
E749653 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Universiti Malaysia Kelantan | Statement: [Universiti Malaysia Kelantan Jeli Campus, campusOf, Universiti Malaysia Kelantan]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Universiti Malaysia Kelantan
Triple: [Universiti Malaysia Kelantan Jeli Campus, campusOf, Universiti Malaysia Kelantan]
Generated description
Universiti Malaysia Kelantan is a Malaysian public university known for its focus on entrepreneurship education and regional development in the state of Kelantan.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69f0bd48691081908cecad39bac591e0 completed April 28, 2026, 1:59 p.m.
NER Named-entity recognition batch_69f66cf3695c81908817bc14126fea9e completed May 2, 2026, 9:30 p.m.
NED1 Entity disambiguation (via context triple) batch_6a262d5ffd708190a3d5f6a909696d01 completed June 8, 2026, 2:48 a.m.
NEDg Description generation batch_6a2632b23e98819091c6cb8f7ab9b505 completed June 8, 2026, 3:10 a.m.
NED2 Entity disambiguation (via description) batch_6a263324bf1c81909c12edd9939b0093 completed June 8, 2026, 3:12 a.m.
Created at: April 28, 2026, 5:08 p.m.